Abstract

Lens-free holographic on-chip imaging is an emerging approach that offers both wide field-of-view (FOV) and high spatial resolution in a cost-effective and compact design using source shifting based pixel super-resolution. However, color imaging has remained relatively immature for lens-free on-chip imaging, since a ‘rainbow’ like color artifact appears in reconstructed holographic images. To provide a solution for pixel super-resolved color imaging on a chip, here we introduce and compare the performances of two computational methods based on (1) YUV color space averaging, and (2) Dijkstra’s shortest path, both of which eliminate color artifacts in reconstructed images, without compromising the spatial resolution or the wide FOV of lens-free on-chip microscopes. To demonstrate the potential of this lens-free color microscope we imaged stained Papanicolaou (Pap) smears over a wide FOV of ~14 mm2 with sub-micron spatial resolution.

Figures (7)

Lens-free color on-chip imaging set-up. A monochromator that is coupled to a multi-mode fiber (0.1 mm core size) serves as the light source. In this geometry, an optoelectronic image sensor (pixel-pitch of 1.12 μm) samples an in-line hologram over the active area of the image sensor. Since Z1 >> Z2 the FOV of the reconstructed image equals to the entire active area of the sensor chip. To improve the spatial resolution of this lens-free color microscope, pixel super resolution is implemented by shifting the source (see the upper left inset). Furthermore, multi-height phase-recovery is implemented by acquiring pixel super-resolved holograms at different object-to-sensor distances (i.e., by varying Z2). Raw color images are obtained by sequential acquisition of red, green and blue in-line holograms.

(a) A lens-free color image that was created by three high-resolution reconstructed holograms, where each hologram was acquired with a different illumination wavelength (λ = 460 nm, 530 nm and 630 nm). The ‘rainbow’ color artifact is evident. (b) Object-support based phase-recovery was applied on each of the three high-resolution holograms, and then the resulting super-resolved images were combined into one RGB color image, where the ‘rainbow’ color artifact is still apparent. (c) The result of colorization method #1 (YUV color space averaging). (d) The result of colorization method #2 that is based on Dijkstra’s shortest path. For both (c) and (d), the ‘rainbow’ color artifact is clearly eliminated, while the spatial resolution is maintained. (e) A 20 × objective (0.5 NA) microscope image of the same sample.

(a) The computational flowchart for acquiring and obtaining a high-resolution (i.e., pixel super-resolved) gray scale image using a single illumination wavelength (λ = 530 nm). (b) The computational flowchart for acquiring and obtaining one lower-resolution color image. This RGB image is then converted into YUV color space, where the color or chrominance channels (UV) are averaged. (c) The high-resolution brightness component from (a) is added to the averaged color components (UV) in (b), and the resulting image is converted into RGB color space to obtain a high-resolution lens-free color image, which provides decent color reproduction without sacrificing spatial resolution.

A block-diagram that describes the computational steps that are preformed in the colorization approach (method #2) based on Dijkstra’s shortest path algorithm. This process automatically assigns color patches, which are later propagated to the entire image FOV.

(a) A lens-free amplitude image of a 1951 USAF resolution test chart, which was acquired using the computational flowchart described in Fig. 3. In this experiment, three high-resolution holograms at different heights (Z2 = 270 μm, 392 μm and 440 μm) were recorded. (b) Zoomed in region of (a) reveals that the entire USAF test chart was resolved. The third element in-group nine corresponds to a grating with a line width of ~0.78 μm. (c), (d) Cross-sections of the vertical and horizontal gratings of element three in group nine, respectively.

A comparison between the YUV color space averaging method (#1) and the method (#2) that is based on Dijkstra’s shortest path. The yellow arrows indicate locations where the YUV color space-averaging method successfully colorized the image, while the Dijkstra’s shortest path based algorithm was less successful for the same arrow locations. A confluent region and a sparse region of the Pap smear sample are shown in (a) and (b), respectively.